Please use this identifier to cite or link to this item: https://scholarbank.nus.edu.sg/handle/10635/226625
Title: A DATA MINING APPROACH IN HANDLING THE BIG DATA OF A BUILDING IN SINGAPORE
Authors: TAN HONG SHENG
Issue Date: 2022
Citation: TAN HONG SHENG (2022). A DATA MINING APPROACH IN HANDLING THE BIG DATA OF A BUILDING IN SINGAPORE. ScholarBank@NUS Repository.
Abstract: The Operation and Maintenance (O&M) phases take up 80% of the building lifecycle. Hence, the O&M phases of a building play a significant role in achieving the government's aim to achieve sustainability. In Singapore, the adoption of Building Information Modelling (BIM) and other technologies such as sensors increases rapidly. Although BIM can digitally integrate various information and these sensors allow FM to collect richer sources of information, it leads to the formation of Big Data (BD). BD is not easy to handle due to its high speed, wide variety and high volume. Traditionally, statistical data analysis methods were adopted to analyze BD in Singapore. However, research shows that the statistical data analysis method is ineffective in analyzing BD and poses limitations. For example, the traditional statistical method is slow and tedious, prone-to-error, incapable of analyzing every single combinati on of data and provides poor-quality results. Hence, this study introduces a BIM-based data Ming approach to analyze BD. With the help of BIM, Data Mining (DM) techniques such as cluster analysis, outlier detection and association mining rules are adopted to analyze big data. To determine whether the proposed DM technique can be adopted in Singapore, this study chooses SDE as a study object because it is a new building and utilizes many sensors, aiming to become a net-zero consumption building. Some case studies were selected to find out whether the proposed approach can be adopted in SDE 4. The case studies show that the DM approach can be adopted in SDE 4 and DM approach is effective, fast, and accurate. However, there are some barriers when adopting DM in Singapore. For example, the DM technique is complicated, it incurs high costs and has data security issues. Subsequently, this study proposes some recommendations for the organization and government to overcome the barrier.
URI: https://scholarbank.nus.edu.sg/handle/10635/226625
Appears in Collections:Bachelor's Theses

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